Workflow Design
AI workflows need examples more than instructions
Good examples make AI outputs more consistent and easier for humans to review.
By JirakJ
6 min read
When a team brings this to me, I listen for ownership before I listen for tooling. Instructions are technically correct but outputs still vary too much. That is the real buying signal.
If the team argues about tooling before inputs and outputs, the meeting is already drifting. For teams designing reusable AI workflows, the practical question is whether the workflow is ready to be made more reliable.
The uncomfortable question
If this workflow disappeared for a week, who would notice first? That person is usually closer to the truth than the AI roadmap is.
The current failure mode
Instructions are technically correct but outputs still vary too much. That is operational debt. AI may make it more visible, but it will not clean it up by itself.
The intervention
Collect accepted, rejected and borderline examples for each output type. Keep it narrow enough that the team can see whether it works within days, not quarters.
The artifact
The artifact I would want is a example library. Without that, the project depends too much on memory and confidence.
Monday morning checklist
- • Write the non-goals. Most bad AI projects expand because nobody says what is out of scope.
- • Write down the artifact that would make the work reviewable: in this case, a example library.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
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